(1) Some researchers (de Ruiter, 2000; Kita & Özyürek, 2003; McNeill, 2000) have suggested that gesture arises pre-linguistically during conceptual preparations for speaking but is influenced by language parameters (such as clause structure and lexical semantics) via feedback from linguistic processing.
Elman and McClelland (1988) reported that lexically restored ambiguous phonemes influenced the perception of the following phoneme, supporting models with feedback from lexical to phonemic representations.
His research concerns a variety of areas in psycholinguistics and applied linguistics, specifically bilingual lexical processing and representation, peer feedback in second language writing, corpus studies, and most recently, test washback in the Japanese context.
The pattern of results supports the idea that lexical bias has two sources, immediate feedback of activation and self-monitoring of inner speech.
Each trial was comprised of four temporally jittered events that were spaced, on average, 4 s apart: anticipatory cue presentation, target presentation, a lexical filler stimulus, and success-dependent feedback (Figure 1).
To illustrate the approach, let us have a look at the data of Stone et al. (1997) on the effects of feedforward and feedback spelling sound consistency in lexical decision (Table 10).
Although this would be the case if all the models licensed feedback from language to thought, the Lexical Hypothesis models posit one-directional processing so, in the case of fluent speech, there is no online interaction between language and conceptualisation.
This paper reports two experiments designed to investigate whether lexical bias in phonological speech errors is caused by immediate feedback of activation, by self-monitoring of inner speech, or by both.
A long running debate about the architecture of the spoken-word recognition system has centered on the locus of lexical effects on phonemic processing: does lexical knowledge influence phoneme perception through feedback, or post-perceptually in a purely feedforward system?
Lexical, prosodic, timing, and gesture features can be used to predict a user's visual feedback during conversational dialog with a robotic or virtual agent.
